GA Based on the UV-Structure Hypothesis and Its Application to JSP. - Kokolo Ikeda; Shigenobu Kobayashi (PPSN, 2000)

Failure of Pareto-based MOEAs: does non-dominated really mean near to optimal? - Kokolo Ikeda; Hajime Kita; Shigenobu Kobayashi (CEC, 2001)

Deterministic Multi-step Crossover Fusion: A Handy Crossover Composition for GAs. - Kokolo Ikeda; Shigenobu Kobayashi (PPSN, 2002)

Independent constraint satisfaction and its application to sewerage system control. - Kokolo Ikeda; Akihiro Nagaiwa; Kei Aoki; Shigenobu Kobayashi (IEEE Congress on Evolutionary Computation, 2003)

Exemplar-based direct policy search with evolutionary optimization. - Kokolo Ikeda (Congress on Evolutionary Computation, 2005)

Instance-Based Policy Search using Binomial Distribution Crossover and Iterated Refreshment. - Chikao Tsuchiya; Kokolo Ikeda; Jun Sakuma; Isao Ono; Shigenobu Kobayashi (IEEE Congress on Evolutionary Computation, 2006)

Hybridization of genetic algorithm and local search in multiobjective function optimization: recommendation of GA then LS. - Ken Harada; Kokolo Ikeda; Shigenobu Kobayashi (GECCO, 2006)

On Socialization of Personal Computing. - Gaku Hagiwara; Kokolo Ikeda; Mikihiko Mori; Tetsutaro Uehara; Hajime Kita (C5, 2007)

State evaluation strategy for exemplar-based policy optimization of dynamic decision problems. - Kokolo Ikeda; Hajime Kita (IEEE Congress on Evolutionary Computation, 2007)

Designing Traffic-Sensitive Controllers for Multi-Car Elevators Through Evolutionary Multi-objective Optimization. - Kokolo Ikeda; Hiromichi Suzuki; Sandor Markon; Hajime Kita (EMO, 2007)

Acceleration of parametric Multi-objective Optimization by an initialization technique for Multi-Objective Evolutionary Algorithms. - Hirotaka Kaji; Kokolo Ikeda; Hajime Kita (IEEE Congress on Evolutionary Computation, 2008)

Socialized Computers and Collaborative Learning. - Mikihiko Mori; Kokolo Ikeda; Gaku Hagiwara; Masaki Saga; Tetsutaro Uehara; Hajime Kita (JSAI, 2008)

Development of a Multiple User Quiz System on a Shared Display. - Masaki Saga; Kokolo Ikeda; Mikihiko Mori; Tetsutaro Uehara; Hajime Kita; Yohei Naya; Naomi Nagata; Hiroto Ueda; Akio Okumura; Terufumi Ohno (C5, 2009)

Uncertainty of constraint function in Evolutionary Multi-objective Optimization. - Hirotaka Kaji; Kokolo Ikeda; Hajime Kita (IEEE Congress on Evolutionary Computation, 2009)

Avoidance of constraint violation for experiment-based evolutionary multi-objective optimization. - Hirotaka Kaji; Kokolo Ikeda; Hajime Kita (IEEE Congress on Evolutionary Computation, 2009)

An Agent-Based Approach to Evaluation of Workflow Support Systems. - Hiroki Saito; Tetsuya Abe; Kokolo Ikeda; Kenji Ohira; Mikihiko Mori; Tetsutaro Uehara; Hajime Kita (C5, 2010)

Accelerated UCT and Its Application to Two-Player Games. - Junichi Hashimoto; Akihiro Kishimoto; Kazuki Yoshizoe; Kokolo Ikeda (ACG, 2011)

Playing PuyoPuyo: Two search algorithms for constructing chain and tactical heuristics. - Kokolo Ikeda; Daisuke Tomizawa; Simon Viennot; Yuu Tanaka (CIG, 2012)

Adaptation of game AIs using Genetic Algorithm: Keeping variety and suitable strength. - Kokolo Ikeda; Yu Tanaka; Simon Viennot; Nguyen Huy Quoc; Yohei Ueda (SCIS&ISIS, 2012)

Extracting Important Patterns for Building State-Action Evaluation Function in Othello. - Huy Nguyen; Kokolo Ikeda; Bac Le (TAAI, 2012)

Efficiency of Static Knowledge Bias in Monte-Carlo Tree Search. - Kokolo Ikeda; Simon Viennot (Computers and Games, 2013)

Further Investigations of 3-Member Simple Majority Voting for Chess. - Kristian Toby Spoerer; Toshihisa Okaneya; Kokolo Ikeda; Hiroyuki Iida (Computers and Games, 2013)

Production of various strategies and position control for Monte-Carlo Go - Entertaining human players. - Kokolo Ikeda; Simon Viennot (CIG, 2013)

Biased random sequence generation for making common player believe it unbiased. - Sila Temsiririrkkul; Hisamitsu Nomura; Kokolo Ikeda (GEM, 2014)

Fast Optimization of the Pattern Shapes in Board Games with Simulated Annealing. - Huy Nguyen; Simon Viennot; Kokolo Ikeda (KSE, 2014)

Machine-Learning of Shape Names for the Game of Go. - Kokolo Ikeda; Takanari Shishido; Simon Viennot (ACG, 2015)

Adaptive Fighting Game Computer Player by Switching Multiple Rule-Based Controllers. - Naoyuki Sato; Sila Temsiririrkkul; Shogo Sone; Kokolo Ikeda (ACIT-CSI, 2015)

A platform for turn-based strategy games, with a comparison of Monte-Carlo algorithms. - Tsubasa Fujiki; Kokolo Ikeda; Simon Viennot (CIG, 2015)

Estimation of player's preference for cooperative RPGs using multi-strategy Monte-Carlo method. - Naoyuki Sato; Kokolo Ikeda; Takayuki Wada (CIG, 2015)

Detection and labeling of bad moves for coaching go. - Kokolo Ikeda; Simon Viennot; Naoyuki Sato (CIG, 2016)

Three types of forward pruning techniques to apply the alpha beta algorithm to turn-based strategy games. - Naoyuki Sato; Kokolo Ikeda (CIG, 2016)

Learning human-like behaviors using neuroevolution with statistical penalties. - Luong Huu Phuc; Kanazawa Naoto; Kokolo Ikeda (CIG, 2017)

Optimized Non-visual Information for Deep Neural Network in Fighting Game. - Nguyen Duc Tang Tri; Vu Quang; Kokolo Ikeda (ICAART (2), 2017)

Survey of How Human Players Divert In-game Actions for Other Purposes: Towards Human-Like Computer Players. - Sila Temsiririrkkul; Naoyuki Sato; Kenta Nakagawa; Kokolo Ikeda (ICEC, 2017)

Designing Policy Network with Deep Learning in Turn-Based Strategy Games. - Tomihiro Kimura; Kokolo Ikeda (ACG, 2019)

Improving Human Players' T-Spin Skills in Tetris with Procedural Problem Generation. - Taishi Oikawa; Chu-Hsuan Hsueh; Kokolo Ikeda (ACG, 2019)

Generation of Diverse Stages in Turn-Based Role-Playing Game using Reinforcement Learning. - SangGyu Nam; Kokolo Ikeda (CoG, 2019)

Procedural Content Generation of Rhythm Games Using Deep Learning Methods. - Yubin Liang; Wanxiang Li; Kokolo Ikeda (ICEC-JCSG, 2019)

Position Control and Production of Various Strategies for Deep Learning Go Programs. - Tianwen Fan; Yuan Shi; Wanxiang Li; Kokolo Ikeda (TAAI, 2019)

Puyo Puyo. - Kokolo Ikeda (Encyclopedia of Computer Graphics and Games, 2019)

Imitating Agents in A Complex Environment by Generative Adversarial Imitation Learning. - Wanxiang Li; Chu-Hsuan Hsueh; Kokolo Ikeda (CoG, 2020)

High-performance Algorithms using Deep Learning in Turn-based Strategy Games. - Tomihiro Kimura; Kokolo Ikeda (ICAART (2), 2020)

Analyses of Tabular AlphaZero on NoGo. - Chu-Hsuan Hsueh; Kokolo Ikeda; Sang-Gyu Nam; I-Chen Wu (TAAI, 2020)

Procedural Maze Generation Considering Difficulty from Human Players' Perspectives. - Keita Fujihira; Chu-Hsuan Hsueh; Kokolo Ikeda (ACG, 2021)

An Efficient Aircraft Boarding Strategy Considering Implementation. - Kenji Uehara; Kunihiko Hiraishi; Kokolo Ikeda (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2021)

Position Control and Production of Various Strategies for Game of Go Using Deep Learning Methods. - Yuan Shi; Tianwen Fan; Wanxiang Li; Chu-Hsuan Hsueh; Kokolo Ikeda (J. Inf. Sci. Eng., 2021)

Playing Good-Quality Games with Weak Players by Combining Programs with Different Roles. - Chu-Hsuan Hsueh; Kokolo Ikeda (CoG, 2022)

Graph Convolutional Networks for Turn-Based Strategy Games. - Wanxiang Li; Houkuan He; Chu-Hsuan Hsueh; Kokolo Ikeda (ICAART (2), 2022)

Generation of Game Stages With Quality and Diversity by Reinforcement Learning in Turn-Based RPG. - Sang-Gyu Nam; Chu-Hsuan Hsueh; Kokolo Ikeda (IEEE Trans. Games, 2022)

Can We Infer Move Sequences in Go from Stone Arrangements? - Chu-Hsuan Hsueh; Kokolo Ikeda (ACG, 2023)

Improving the Human-Likeness of Game AI's Moves by Combining Multiple Prediction Models. - Tatsuyoshi Ogawa; Chu-Hsuan Hsueh; Kokolo Ikeda (ICAART (3), 2023)

Procedural Content Generation of Super Mario Levels Considering Natural Connection. - SangGyu Nam; Chu-Hsuan Hsueh; Kokolo Ikeda (JCSSE, 2023)

Factor Analyses on Positive and Negative Evaluations of Games against Go Programs. - Kyota Kuboki; Chu-Hsuan Hsueh; Kokolo Ikeda (TAAI (1), 2023)

Analyses of Tabular AlphaZero on Strongly-Solved Stochastic Games. - Chu-Hsuan Hsueh; Kokolo Ikeda; I-Chen Wu; Jr-Chang Chen; Tsan-sheng Hsu (IEEE Access, 2023)

Optimal Execution Strategy Using Deep Q-Network with Heuristics Policy. - Tatsuyoshi Ogawa; Kei Nakagawa; Kokolo Ikeda (IIAI-AAI, 2024)

Improvement of move naturalness for playing good-quality games with middle-level players. - Chu-Hsuan Hsueh; Kokolo Ikeda (Appl. Intell., 2024)

Using Reinforcement Learning to Generate Levels of Super Mario Bros. With Quality and Diversity. - SangGyu Nam; Chu-Hsuan Hsueh; Pavinee Rerkjirattikal; Kokolo Ikeda (IEEE Trans. Games, 2024)

More Human-Like Gameplay by Blending Policies From Supervised and Reinforcement Learning. - Tatsuyoshi Ogawa; Chu-Hsuan Hsueh; Kokolo Ikeda (IEEE Trans. Games, 2024)

Proposal and generation of endgame puzzles for an imperfect information game Geister. - Chu-Hsuan Hsueh; Takefumi Ishii; Tsuyoshi Hashimoto; Kokolo Ikeda (Entertain. Comput., 2025)

Policies of Multiple Skill Levels for Better Strength Estimation in Games. - Kyota Kuboki; Tatsuyoshi Ogawa; Chu-Hsuan Hsueh; Shi-Jim Yen; Kokolo Ikeda (CoRR, 2025)